Showing posts with label Math. Show all posts
Showing posts with label Math. Show all posts
Discrete Mathematics 1
Monday, August 4, 2025
Your first course in DM and mathematical literacy: logic, sets, proofs, functions, relations, and intro to combinatorics
Preview this Course
Description
Discrete Mathematics 1
Mathematics from high school to university
S1. Introduction to the course
You will learn: about this course: its content and the optimal way of studying it together with the book.
S2. Preliminaries: "paintbrushes and easels"
You will learn: some basics needed for understanding the new topics discussed in this course.
S3. A very soft start: "painting happy little trees"
You will learn: you will get the first glimpse into various types of problems and tricks that are specific to Discrete Mathematics: proving formulas, motivating formulas, deriving formulas, generalising formulas, a promise of Mathematical Induction, divisibility of numbers (by factoring, by analysing remainders / cases), various ways of dealing with problem solving (mathematical modelling, using graphs, using charts, using Pigeonhole Principle, using the Minimum Principle, [always] using logical thinking; strategies); you will also see some problems that we are not able to solve (yet) but which will be solved in Section 12 where we will learn more about counting; nothing here is based on big theories, just logical thinking; treat it as a "smörgåsbord" (Swedish buffet) of Discrete Mathematics.
S4. Logic
You will learn: the meaning of the symbols used in logic; conjunction, disjunction, implication, equivalence, negation; basic rules of logic (tautologies) and how to prove them; two kinds of quantifiers: existential and universal; necessary and sufficient conditions. This section is almost identical to Section 7 in "Precalculus 1: Basic notions"; I have only removed the material covering the epsilon-delta definition of limit, because it is irrelevant for Discrete Mathematics. I have also added several new problems (Videos 87-93) that were not present in the Precalculus course.
S5. Sets
You will learn: the basic terms and formulas from the Set Theory and the link to Logic; union, intersection, set difference, subset, complement; cardinality of a set; Inclusion-exclusion principle. This section is almost identical to Section 8 in "Precalculus 1: Basic notions".
S6. Functions
You will learn: about functions: various ways of defining functions; domain, codomain, range, graph; surjections, injections, bijections, inverse functions, inverse images; bijections and cardinality of sets; compositions of functions; some examples of monotone and periodic functions. You will get an information about other topics relevant for examining functions (in Calculus) and where to find them (in the Precalculus and Calculus series).
S7. Relations
You will learn: about binary relations generally, and specifically about RST (Reflexive-Symmetric-Transitive) relations, equivalence classes, and about order (partial order) relations. This section is almost identical to Section 9 in "Precalculus 1: Basic notions".
S8. Functions as relations
You will learn: definition of a function as relation between sets: domain and co-domain; injections, surjections, bijections, inverse functions. This section is almost identical to Section 10 in "Precalculus 1: Basic notions".
S9. A very brief introduction to sequences
You will learn: you will get an extremely brief introduction to the topic of sequences: just enough for the next sections in this course (proofs and combinatorics; for the latter we only need finite sequences) and to complete the discussion of functions defined on the set of natural numbers (adding and scaling sequences, monotone sequences); the topic of sequences will be covered very thoroughly and from scratch in DM2.
S10. Various proof techniques
You will learn: the meaning of words axiom, definition, theorem, lemma, proposition, corollary, proof; various types of proofs with some examples: direct proof, indirect proof (proof by contradiction, proof by contrapositive), proof by induction, proof by cases; proving or disproving statements (finding counterexamples).
S11. Factorial, n choose k, and Binomial Theorem
You will learn: some important properties of the sigma symbol; the factorial function, binomial coefficients, Pascal's Triangle; the Binomial Theorem (theorem telling you how to raise a sum of two terms to any positive natural power) with a motivation and a formal proof; I will show you (mostly without proving, as this is moved to DM2, where you will see plenty of proofs, both "regular" and combinatorial) several binomial identities and where you can find even more of them; all this will be really important for the next section (Combinatorics).
S12. Combinatorics: the art of counting, an introduction (TBC in DM2)
You will learn: some basic combinatorial concepts like permutation, variation, and combination; an explanation of the name of binomial coefficients ("n choose k"); counting subsets of a finite set; counting paths on a grid; some examples of combinatorial proofs. Much more follows in DM2, I promise.
Note: This is the first part of our trilogy in Discrete Mathematics. The following subjects will be covered in the next courses: an introduction to Number Theory with modular arithmetic, an introduction to algebraic structures (groups, rings, fields, etc), with groups of permutations and really cool geometrical applications (DM2); sequences (recurrences, generating functions, etc), an introduction to Graph Theory (DM3). The topic of Combinatorics will be further (after the introduction done in DM1) discussed in DM2, followed by a very brief introduction to (discrete) probability.
Make sure that you check with your professor what parts of the course you will need for your final exam. Such things vary from country to country, from university to university, and they can even vary from year to year at the same university.
A detailed description of the content of the course, with all the 272 videos and their titles, and with the texts of all the 395 problems solved during this course, is presented in the resource file
“001 List_of_all_Videos_and_Problems_Discrete_Mathematics_1.pdf”
under Video 1 ("Introduction to the course"). This content is also presented in Video 1.
Who this course is for:
- Students who plan to study mathematics at a college or university level, and want to gain some mathematical maturity to succeed
- High school students curious about university mathematics; the course is intended for purchase by adults for these students
- Everybody who wants to brush up their high school maths and gain a deeper understanding of the subject
- College and university students studying advanced courses, who want to understand all the details they might have missed in their earlier education
- Students who need mathematics for Computer Science, AI, ML, or other domains where one needs maths
The Online AP Statistics Course
Friday, February 4, 2022
The Online AP Statistics Course - The Online AP Statistics Course places an emphasis on challenging problem-solving questions and concise video lectures.
Rhomi Tutoring
New
What you'll learn
- Learn to choose which methods for collecting or analyzing data
- Learn to describe patterns, trends, associations, and relationships in data
- Learn to use probability and simulation to describe probability distributions and define uncertainty in statistical inference
- Learn to use statistical reasoning to draw conclusions
Description
Hello everyone!
My name is Sunny and I've been a Statistics tutor for the past 4 years. You may know me from the maker of The Online AP Chemistry Course. As a 5-scorer on the AP Statistics Exam myself, I've invested hundreds of hours in tutoring dozens of students in their AP coursework. In 2019 I started RhomiTutoring, which provides online 1-on-1 tutoring to students around the world. AP Statistics can be a particularly difficult subject for students who are unfamiliar with statistical topics. While it is a "mathematical" subject, you will quickly find that statistics is as much about inference and probability as it is about finding the objective "right answer". At present, there are many educational videos you can find on AP Statistics but few are structured in such a way that follows a detailed curriculum, and the vast majority don't provide practice problems to students. Or worse, the practice problems are simply not of the difficulty that would be needed to achieve a 5.
That's why this course was designed to have an emphasis on problem sets. It contains 6 broad topics, with each topic containing quizzes, lectures and 40+ exercises, along with hours of lectures and several problem sets.
1. Data Analysis
2. Normal Curves and Z-scores
3. Scatterplots, Correlation, and Relationships
4. Probability
5. Sampling and Confidence Intervals
6. Acids and Bases
The Online AP Statistics Course is not associated or endorsed by Collegeboard in any way.
Who this course is for:
- Individuals looking to learn statistics, with an emphasis on Collegeboard's AP Curriculum
Become a Probability & Statistics Master
Tuesday, January 25, 2022
Free Coupon Discount - Become a Probability & Statistics Master, Learn everything from Probability & Statistics, then test your knowledge with 600+ practice questions
BESTSELLER, 4.6 (2,514 ratings), Created by Krista King, English [Auto-generated], French [Auto-generated], 4 more
PREVIEW THIS COURSE - GET COUPON CODE
Description
HOW BECOME A PROBABILITY & STATISTICS MASTER IS SET UP TO MAKE COMPLICATED MATH EASY:
This 163-lesson course includes video and text explanations of everything from Probability and Statistics, and it includes 45 quizzes (with solutions!) and an additional 8 workbooks with extra practice problems, to help you test your understanding along the way. Become a Probability & Statistics Master is organized into the following sections:
Visualizing data, including bar graphs, pie charts, Venn diagrams, histograms, and dot plots
Analyzing data, including mean, median, and mode, plus range and IQR and box-and-whisker plots
Data distributions, including mean, variance, and standard deviation, and normal distributions and z-scores
Probability, including union vs. intersection and independent and dependent events and Bayes' theorem
Discrete random variables, including binomial, Bernoulli, Poisson, and geometric random variables
Sampling, including types of studies, bias, and sampling distribution of the sample mean or sample proportion, and confidence intervals
Hypothesis testing, including inferential statistics, significance levels, type I and II errors, test statistics, and p-values
Regression, including scatterplots, correlation coefficient, the residual, coefficient of determination, RMSE, and chi-square
AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:
Videos: Watch over my shoulder as I solve problems for every single math issue you’ll encounter in class. We start from the beginning... I explain the problem setup and why I set it up that way, the steps I take and why I take them, how to work through the yucky, fuzzy middle parts, and how to simplify the answer when you get it.
Notes: The notes section of each lesson is where you find the most important things to remember. It’s like Cliff Notes for books, but for math. Everything you need to know to pass your class and nothing you don’t.
Quizzes: When you think you’ve got a good grasp on a topic within a course, you can test your knowledge by taking one of the quizzes. If you pass, great! If not, you can review the videos and notes again or ask for help in the Q&A section.
Workbooks: Want even more practice? When you've finished the section, you can review everything you've learned by working through the bonus workbook. The workbooks include tons of extra practice problems, so they're a great way to solidify what you just learned in that section.
HERE'S WHAT SOME STUDENTS OF BECOME A PROBABILITY & STATISTICS MASTER HAVE TOLD ME:
“Krista is an experienced teacher who offers Udemy students complete subject matter coverage and efficient and effective lessons/learning experiences. She not only understands the course material, but also selects/uses excellent application examples for her students and presents them clearly and skillfully using visual teaching aids/tools.” - John
“Really good, thorough, well explained lessons.” - Scott F.
“This is my second course (algebra previously) from Ms. King's offerings. I enjoyed this course and learned a lot! Each video explains a concept, followed by the working of several examples. I learned the most by listening to Ms King's teaching of the concept, stopping the video, and then attempting to work the example problems. After working the problems, then watching her complete the examples, I found that I really retained the concepts. A great instructor!” - Charles M.
YOU'LL ALSO GET:
Lifetime access to Become a Probability & Statistics Master
Friendly support in the Q&A section
Udemy Certificate of Completion available for download
30-day money back guarantee
Enroll today!
I can't wait for you to get started on mastering probability and statistics.
- Krista :)
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Mathematical Foundations of Machine Learning
Tuesday, November 16, 2021
Mathematical Foundations of Machine Learning - Essential Linear Algebra and Calculus Hands-On in NumPy, TensorFlow, and PyTorch
What you'll learn
- Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science
- Manipulate tensors using all three of the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch
- How to apply all of the essential vector and matrix operations for machine learning and data science
- Reduce the dimensionality of complex data to the most informative elements with eigenvectors, SVD, and PCA
- Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion)
- Appreciate how calculus works, from first principles, via interactive code demos in Python
- Intimately understand advanced differentiation rules like the chain rule
- Compute the partial derivatives of machine-learning cost functions by hand as well as with TensorFlow and PyTorch
- Grasp exactly what gradients are and appreciate why they are essential for enabling ML via gradient descent
- Use integral calculus to determine the area under any given curve
- Be able to more intimately grasp the details of cutting-edge machine learning papers
- Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning
Requirements
- All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.
- Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information — such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics.
Description
Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math.
Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. But understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increase the impact you can make over the course of your career.
Led by deep learning guru Dr. Jon Krohn, this course provides a firm grasp of the mathematics — namely linear algebra and calculus — that underlies machine learning algorithms and data science models.
Course Sections
Linear Algebra Data Structures
Tensor Operations
Matrix Properties
Eigenvectors and Eigenvalues
Matrix Operations for Machine Learning
Limits
Derivatives and Differentiation
Automatic Differentiation
Partial-Derivative Calculus
Integral Calculus
Throughout each of the sections, you'll find plenty of hands-on assignments, Python code demos, and practical exercises to get your math game in top form!
This Mathematical Foundations of Machine Learning course is complete, but in the future, we intend on adding bonus content from related subjects beyond math, namely: probability, statistics, data structures, algorithms, and optimization. Enrollment now includes free, unlimited access to all of this future course content — over 25 hours in total.
Are you ready to become an outstanding data scientist? See you in the classroom.
Who this course is for:
- You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities
- You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems
- You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline
- You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!)
Probability and Statistics 1: The Complete Guide
Monday, May 17, 2021
Probability and Statistics 1: The Complete Guide - Learn everything fast through concise yet contented lectures
- Created by L Sang
- English [Auto]
What you'll learn
- representation and characterisation of data
- elementary probability theory
- combinatorics
- rules for computing probabilities
- conditional probability and independence of events
- discrete random variables and their probability distributions
- expectation value of a discrete random variable
- examples of discrete random variables
- moment generating functions
- continuous random variables and their probability distributions
- expectation value of a continuous random variable
- examples of continuous random variables
- Chebyshev's theorem
- bivariate probability distributions
- expectations of functions of random variables
- covariance
- independent random variables
- sums of independent random variables
- hypothesis testing
Description
I know, Probability and Statistics is difficult. But is there a way to make it easy? Of course. I for one managed that.
I know a lot of people struggle with it; a very small group of people are good at it. Back in university, I was in that bigger group, the group that struggled through Probability and Statistics lecture. I needed help; I couldn't understand a thing, but I finally found help and turned my exam result around. I guess since you're looking at this, you need help too.
This 6-hour COMPLETE GUIDE course contains everything you need to know to get started with Probability and Statistics. It's packed with videos that have been categorised into different topics, hence easy for you to learn.
I've included lots of definitions, theorems, quizzes, examples, concise notes for EVERY single section, exercises, and a walkthrough of all the exercise sheets. Most importantly, I've done a BONUS section for you! It includes some additional questions that will strengthen your skill even more.
With this basic Probability and Statistics course, you will have a good core understanding to pursue many more difficult Mathematics topic. In this course, everything has been broken down into a simple structure to make learning and understanding easy for you.
This COMPLETE guide is for those of you are looking to get a full understanding of the basics; the important parts. You've already shown half of your determination by looking at the course, so if this course sounds right for you, boost your eagerness to learn and join me on this journey!
Tips:
1) It will be very useful if you also take notes of your own as you're watching the lectures, it will help you understand everything better and quicker. Just pause if I move on to other topics too fast or if you haven't fully understood the previous sub-topic before you move on to the next parts.
2) Please ask any questions you may have in the Q&A section if you don't understand. It's one thing to not understand it, but it's a whole new experience and a very important thing to do when learning Maths to be able to discuss it with fellow students who are going through the same thing.
3) Use headphones for better sound. (I suggest you turn the volume up)
4) Don't forget you can always slow down or speed up the video!
Who this course is for:
high school students
university students
courses that require probability and statistics (e.g. computer science, and other sciences)
Complete linear algebra: theory and implementation in code
Tuesday, April 27, 2021
Free Coupon Discount - Complete linear algebra: theory and implementation in code, Learn concepts in linear algebra and matrix analysis, and implement them in MATLAB and Python. | Created by Mike X Cohen
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Description
You need to learn linear algebra!
Linear algebra is perhaps the most important branch of mathematics for computational sciences, including machine learning, AI, data science, statistics, simulations, computer graphics, multivariate analyses, matrix decompositions, signal processing, and so on.
You need to know applied linear algebra, not just abstract linear algebra!
The way linear algebra is presented in 30-year-old textbooks is different from how professionals use linear algebra in computers to solve real-world applications in machine learning, data science, statistics, and signal processing. For example, the "determinant" of a matrix is important for linear algebra theory, but should you actually use the determinant in practical applications? The answer may surprise you, and it's in this course!
If you are interested in learning the mathematical concepts linear algebra and matrix analysis, but also want to apply those concepts to data analyses on computers (e.g., statistics or signal processing), then this course is for you! You'll see all the maths concepts implemented in MATLAB and in Python.
Unique aspects of this course
Clear and comprehensible explanations of concepts and theories in linear algebra.
Several distinct explanations of the same ideas, which is a proven technique for learning.
Visualization using graphs, numbers, and spaces that strengthens the geometric intuition of linear algebra.
Implementations in MATLAB and Python. Com'on, in the real world, you never solve math problems by hand! You need to know how to implement math in software!
Beginning to intermediate topics, including vectors, matrix multiplications, least-squares projections, eigendecomposition, and singular-value decomposition.
Strong focus on modern applications-oriented aspects of linear algebra and matrix analysis.
Intuitive visual explanations of diagonalization, eigenvalues and eigenvectors, and singular value decomposition.
Improve your coding skills! You do need to have a little bit of coding experience for this course (I do not teach elementary Python or MATLAB), but you will definitely improve your scientific and data analysis programming skills in this course. Everything is explained in MATLAB and in Python (mostly using numpy and matplotlib; also sympy and scipy and some other relevant toolboxes).
Benefits of learning linear algebra
Understand statistics including least-squares, regression, and multivariate analyses.
Improve mathematical simulations in engineering, computational biology, finance, and physics.
Understand data compression and dimension-reduction (PCA, SVD, eigendecomposition).
Understand the math underlying machine learning and linear classification algorithms.
Deeper knowledge of signal processing methods, particularly filtering and multivariate subspace methods.
Explore the link between linear algebra, matrices, and geometry.
Gain more experience implementing math and understanding machine-learning concepts in Python and MATLAB.
Why I am qualified to teach this course:
I have been using linear algebra extensively in my research and teaching (in MATLAB and Python) for many years. I have written several textbooks about data analysis, programming, and statistics, that rely extensively on concepts in linear algebra.
So what are you waiting for??
Watch the course introductory video and free sample videos to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up.
I hope to see you soon in the course!
Mike
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Master Math by Coding in Python
Wednesday, January 20, 2021
Free Coupon Discount - Master Math by Coding in Python, Use Python to learn algebra, calculus, graphing, trigonometry and more math topics!
Created by Codestars by Rob Percival Mike X Cohen
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Description
You can learn a lot of math with a bit of coding!
Many people don't know that Python is a really powerful tool for learning math. Sure, you can use Python as a simple calculator, but did you know that Python can help you learn more advanced topics in algebra, calculus, and matrix analysis? That's exactly what you'll learn in this course.
This course is a perfect supplement to your school/university math course, or for your post-school return to mathematics.
Let me guess what you are thinking:
"But I don’t know Python!" That’s okay! This course is aimed at complete beginners; I take you through every step of the code. You don't need to know anything about Python, although it's useful if you already have some programming experience.
"But I’m not good at math!" You will be amazed at how much better you can learn math by using Python as a tool to help with your courses or your independent study. And that's exactly the point of this course: Python programming as a tool to learn mathematics. This course is designed to be the perfect addition to any other math course or textbook that you are going through.
What do you get in this course?
Over 33 hours of instruction that includes Python coding, visualization, loops, variables, and functions.
LOTS of practical exercises! Each video has at least one hands-on coding/math exercise (and you'll get to watch me solve those exercises). And each section ends with "bug hunts" where you get to find and fix my math-coding errors!
That warm, fuzzy feeling of confidence that you can combine the skills from this course to improve your understanding of mathematics.
A big-picture overview of beginner and advanced mathematics, from solving for "x" to computing integrals to finding eigenvalues. If you are only just beginning your adventures in maths, then this course will show you what you have to look forward to!
This course is right for you if you are:
In middle/high school, university, or are returning to math as an independent learner.
A data professional who wants to brush up on math and Python skills.
A complete beginner to Python.
Already proficient with math "in theory" and want to learn how to translate math formulas and concepts into computer code.
Bored and looking for a fun intellectual challenge.
With over 31 hours of teaching, plus student exercises, challenges and an active course Q&A forum (get a response to any question within 48 hours!), this course gives you everything you need to succeed in your maths course or independent adventures in learning math.
All the code that appears in the videos is also included for download. You can code along as you watch the videos, or download the code and use it directly.
This course covers the following topics:
Arithmetic
Introduction to Sympy
Introduction to LaTeX (to print beautiful equations!)
Algebra 1
Graphing
Algebra 2
Graphing conic sections
Trigonometry
Calculus
Linear algebra
...and more!
Who is your teacher?
I am Mike X Cohen, an associate professor at the Radboud University (the Netherlands). I'm a bestselling and highly rated instructor on Udemy. I've taught over 73,000 students the foundations of scientific programming, data analysis, and applied mathematics, and I've written several textbooks on programming and data analyses.
I worked really hard to make this course a great learning experience for you. Check out what some of my students have said about my other courses:
***** ‘Best teacher ever. I am a psychologist and I didn’t have mathematical training as an undergrad, but the books and lectures of Dr. Cohen have been life saving’
***** ‘What I REALLY like about Mike's style is that not only clear and direct, but he mixes in appropriate amounts of foreshadowing … to make it easier for me to connect the dots.’
***** ‘Mike X Cohen's courses are by far the best ones I've done in Udemy.’
What you should do right now:
Watch the free preview videos.
Check out the reviews of this course.
Joining this course is risk-free: If you change your mind after enrolling, Udemy offer a 30 day money back guarantee, and you can find full details here: https://support.udemy.com
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A Comprehensive Guide to Bayesian Statistics
Sunday, November 8, 2020
Preview this Udemy Course
A Comprehensive Guide to Bayesian Statistics - Bayesian Inference, Prior & Posterior Distn, Bayesian Interval Estimation, Bayesian Hypothesis Testing & Decision Theory
This course is a comprehensive guide to Bayesian Statistics. It includes video explanations along with real life illustrations, examples, numerical problems, take away notes, practice exercise workbooks, quiz, and much more . The course covers the basic theory behind probabilistic and Bayesian modelling, and their applications to common problems in data science, business, and applied sciences.
The course is divided into the following sections:
Section 1 and 2: These two sections cover the concepts that are crucial to understand the basics of Bayesian Statistics-
An overview on Statistical Inference/Inferential Statistics
Introduction to Bayesian Probability
Frequentist/Classical Inference vs Bayesian Inference
Bayes Theorem and its application in Bayesian Statistics
Real Life Illustrations of Bayesian Statistics
Key concepts of Prior and Posterior Distribution
Types of Prior
Solved numerical problems addressing how to compute the posterior probability distribution for population parameters
Conjugate Prior
Jeffrey's Non-Informative Prior
Section 3: This section covers Interval Estimation in Bayesian Statistics:
Confidence Intervals in Frequentist Inference vs Credible Intervals in Bayesian Inference
Interpretation of Confidence Intervals & Credible Intervals
Computing Credible Interval for Posterior Mean
Section 4: This section covers Bayesian Hypothesis Testing:
Introduction to Bayes Factor
Interpretation of Bayes Factor
Solved Numerical problems to obtain Bayes factor for two competing hypotheses
Section 5: This section caters to Decision Theory in Bayesian Statistics:
Basics of Bayesian Decision Theory with examples
Decision Theory Terminology: State/Parameter Space, Action Space, Decision Rule. Loss Function
Real Life Illustrations of Bayesian Decision Theory
Classification Loss Matrix
Minimizing Expected Loss
Decision making with Frequentist vs Bayesian approach
Types of Loss Functions: Squared Error Loss, Absolute Error Loss, 0-1 Loss
Bayesian Expected Loss
Risk : Frequentist Risk/Risk Function, Bayes Estimate, and Bayes Risk
Admissibility of Decision Rules
Procedures to find Bayes Estimate & Bayes Risk: Normal & Extensive Form of Analysis
Solved numerical problems of computing Bayes Estimate and Bayes Risk for different Loss Functions
Section 6: This section includes:
Bayesian's Defense & Critique
Applications of Bayesian Statistics in various fields
Additional Resources
Bonus Lecture and a Quiz
At the end of the course, you will have a complete understanding of Bayesian concepts from scratch. You will know how to effectively use Bayesian approach and think probabilistically. Enrolling in this course will make it easier for you to score well in your exams or apply Bayesian approach elsewhere.
Complete this course, master the principles, and join the queue of top Statistics students all around the world.
Become a Calculus 1 Master
Free Coupon Discount - Become a Calculus 1 Master, Learn everything from Calculus 1, then test your knowledge with 600+ practice questions | Created byKrista King
Preview this Udemy Course - GET COUPON CODE
Description
HOW BECOME A CALCULUS 1 MASTER IS SET UP TO MAKE COMPLICATED MATH EASY:
This 395-lesson course includes video and text explanations of everything from Calculus 1, and it includes 110 quizzes (with solutions!) and an additional 28 workbooks with extra practice problems, to help you test your understanding along the way. Become a Calculus 1 Master is organized into the following sections:
Precalculus
Limits & Continuity
Derivatives
Applications of Derivatives
AND HERE'S WHAT YOU GET INSIDE OF EVERY SECTION:
Videos: Watch over my shoulder as I solve problems for every single math issue you’ll encounter in class. We start from the beginning... I explain the problem setup and why I set it up that way, the steps I take and why I take them, how to work through the yucky, fuzzy middle parts, and how to simplify the answer when you get it.
Notes: The notes section of each lesson is where you find the most important things to remember. It’s like Cliff Notes for books, but for math. Everything you need to know to pass your class and nothing you don’t.
Quizzes: When you think you’ve got a good grasp on a topic within a course, you can test your knowledge by taking one of the quizzes. If you pass, great! If not, you can review the videos and notes again or ask for help in the Q&A section.
Workbooks: Want even more practice? When you've finished the section, you can review everything you've learned by working through the bonus workbook. The workbooks include tons of extra practice problems, so they're a great way to solidify what you just learned in that section.
HERE'S WHAT SOME STUDENTS OF BECOME A CALCULUS 1 MASTER HAVE TOLD ME:
“This course is absolutely amazing, I use both this course and the calc 2 course to be able to keep up with accelerated, without this i would be screwed. She's very easy to understand.” - Dean V.
“I'm really enjoying how the course has been simplified and made easy to understand. The quizzes after every section helped solidify the concepts. Everything is explained in detail and with great simplicity. I really like the way Krista teaches, It's quite clear, straightforward and easy to understand. It's comprehensive interactive course and totally worth the time and money! I bought all calculus volumes.” - Ghaith A.
“Very well-made. Instructor explained everything clearly; found no difficulties understanding topics of study through instructor's teaching methods. Done very well, overall! Glad to have invested in this course!” - Anish S.
“AWESOME TUTORIALS! GREAT COURSE! DEFINITELY RECOMMENDED!” - Bonnie H.
“There is very clear instructions. I'm learning this before actually taking calculus in college so I can have a deeper understanding of math (I'm a math major). She explains everything very thoroughly and works through every problem as if you're a beginner. I will say that I am terrible at receiving audible information but brilliant with someone showing me how to do something on a board and she makes it very clear at understanding everything. I am very pleased to have this much course coverage. I feel like I have my own personal tutor without the expense. Btw if you like math, trigonometric identities are fun!” - Christian R.
“I am very satisfied with this course. It is very clear, delivers all the interesting topics I like that I am taken from knowing little to actually knowing the calculus including the applied mathematics too. This is only Calculus I but it gives me not only sense of accomplishment and understanding but also the foundation for the future courses.” - Robert B.
“I'm self studying mathematics for my electrical engineering degree and this course has been very VERY helpful. I find myself doing these videos before my homework. It makes me want to keep learning. Thank you Krista.” - Lester S.
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I can't wait for you to get started on mastering calculus 1.
- Krista :)
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